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Divide and Conquer: Hybrid Pre-training for Person Search

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arXiv 2023年

作者： Tian, Yanling Chen, Di Liu, Yunan Yang, Jian Zhang, Shanshan PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Artificial Intelligence Dalian Maritime University China

Large-scale pre-training has proven to be an effective method for improving performance across different tasks. Current person search methods use imageNet pre-trained models for feature extraction, yet it is not an optimal solution due to the gap between the pre-training task and person search task (as a downstream task). Therefore, in this paper, we focus on pretraining for person search, which involves detecting and reidentifying individuals simultaneously. Although labeled data for person search is scarce, datasets for two sub-tasks person detection and re-identification are relatively abundant. To this end, we propose a hybrid pre-training framework specifically designed for person search using sub-task data only. It consists of a hybrid learning paradigm that handles data with different kinds of supervisions, and an intra-task alignment module that alleviates domain discrepancy under limited resources. To the best of our knowledge, this is the first work that investigates how to support full-task pre-training using sub-task data. Extensive experiments demonstrate that our pre-trained model can achieve significant improvements across diverse protocols, such as person search method, finetuning data, pre-training data and model backbone. For example, our model improves ResNet50 based NAE by 10.3% relative improvement w.r.t. mAP. Our code and pre-trained models are released for plug-and-play usage to the person search community (https://***/personsearch/PretrainPS). Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Center-Based Decoupled Point Cloud Registration for 6D Object Pose Estimation

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Center-Based Decoupled Point Cloud Registration for 6D Objec...

International Conference on Computer Vision (ICCV)

作者： Haobo Jiang Zheng Dang Shuo Gu Jin Xie Mathieu Salzmann Jian Yang PCA Lab Nanjing University of Science and Technology China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China CVLab EPFL Switzerland

In this paper, we propose a novel center-based decoupled point cloud registration framework for robust 6D object pose estimation in real-world scenarios. Our method decouples the translation from the entire transformation by predicting the object center and estimating the rotation in a center-aware manner. This center offset-based translation estimation is correspondence-free, freeing us from the difficulty of constructing correspondences in challenging scenarios, thus improving robustness. To obtain reliable center predictions, we use a multi-view (bird’s eye view and front view) object shape description of the source-point features, with both views jointly voting for the object center. Additionally, we propose an effective shape embedding module to augment the source features, largely completing the missing shape information due to partial scanning, thus facilitating the center prediction. With the center-aligned source and model point clouds, the rotation predictor utilizes feature similarity to establish putative correspondences for SVD-based rotation estimation. In particular, we introduce a center-aware hybrid feature descriptor with a normal correction technique to extract discriminative, part-aware features for high-quality correspondence construction. Our experiments show that our method outperforms the state-of-the-art methods by a large margin on real-world datasets such as TUD-L, LINEMOD, and Occluded-LINEMOD. Code is available at https://***/JiangHB/CenterReg.

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Cascading Enhancement Representation for Face Anti-Spoofing

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SSRN

SSRN 2023年

作者： Ma, Yimei Dong, Yangwei Qian, Jianjun Wong, Wai Keung Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China Institute of Textiles and Clothing The Hong Kong Polytechnic University Hong Kong

Face anti-spoofing (FAS) is the first security line of defense in face recognition system. The majority of current methods focus on distinguishing the live faces from spoof faces by designing adaptive network with auxiliary information to enhance the feature discrimination, which shows that how to achieve the discriminative representation is also vital to solve FAS task. In this paper, motivated by the idea of cascading enhancement, we propose a novel cascading enhancement representation network (CERN) for effective FAS. Specifically, the CERN utilizes two branches to achieve multi-level feature in cascading enhancement feature extraction stage. The first branch employs the backbone network to concatenate the multi-scale feature in conjunction with attention modules. The second branch utilizes the shared attention modules to enhance the input space for learning the multi-level refinement features. In cascading enhancement feature fusion stage, we transmit the high-level feature to the middle level (mid-level) for enhancing the mid-level representation. The novel mid-level feature is then used to enhance the low-level feature. Moreover, the weight map learning scheme is proposed to further enhance discrimination of the predicted binary map. Additionally, we use meta learning to extend our CERN for solving cross-database testing. Experiments on five benchmark databases demonstrate the effectiveness of our methods against the state-of-art methods. © 2023, The Authors. All rights reserved.

关键词： Face recognition

Graph Matching Optimization Network for Point Cloud Registration

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Graph Matching Optimization Network for Point Cloud Registra...

IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Qianliang Wu Yaqi Shen Haobo Jiang Guofeng Mei Yaqing Ding Lei Luo Jin Xie Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia

Point Cloud Registration is a fundamental and challenging problem in 3D computer vision. Recent works often utilize geometric structure features in downsampled points (patches) to seek correspondences, then propagate these sparse patch correspondences to the dense level in the corresponding patches' neighborhood. However, they neglect the explicit global scale rigid constraint at the dense level point matching. We claim that the explicit isometry-preserving constraint in the dense level on a global scale is also important for improving feature representation in the training stage. To this end, we propose a Graph Matching Optimization based Network (GMONet for short), which utilizes the graph-matching optimizer to explicitly exert the isometry preserving constraints in the point feature training to improve the point feature representation. Specifically, we exploit a partial graph-matching optimizer to enhance the super point (i.e., down-sampled key points) features and a full graph-matching optimizer to improve the dense level point features in the overlap region. Meanwhile, we leverage the inexact proximal point method and the mini-batch sampling technique to accelerate these two graph-matching optimizers. Given high discriminative point features in the evaluation stage, we utilize the RANSAC approach to estimate the transformation between the scanned pairs. The proposed method has been evaluated on the 3DMatch/3DLoMatch and the KITTI datasets. The experimental results show that our method performs competitively compared to state-of-the-art baselines.

关键词：

SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation

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arXiv 2023年

作者： Jiang, Haobo Salzmann, Mathieu Dang, Zheng Xie, Jin Yang, Jian PCA Lab Nanjing University of Science and Technology China CVLab EPFL Switzerland ClearSpace Switzerland PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

In this paper, we introduce an SE(3) diffusion model-based point cloud registration framework for 6D object pose estimation in real-world scenarios. Our approach formulates the 3D registration task as a denoising diffusion process, which progressively refines the pose of the source point cloud to obtain a precise alignment with the model point cloud. Training our framework involves two operations: An SE(3) diffusion process and an SE(3) reverse process. The SE(3) diffusion process gradually perturbs the optimal rigid transformation of a pair of point clouds by continuously injecting noise (perturbation transformation). By contrast, the SE(3) reverse process focuses on learning a denoising network that refines the noisy transformation step-by-step, bringing it closer to the optimal transformation for accurate pose estimation. Unlike standard diffusion models used in linear Euclidean spaces, our diffusion model operates on the SE(3) manifold. This requires exploiting the linear Lie algebra se(3) associated with SE(3) to constrain the transformation transitions during the diffusion and reverse processes. Additionally, to effectively train our denoising network, we derive a registration-specific variational lower bound as the optimization objective for model learning. Furthermore, we show that our denoising network can be constructed with a surrogate registration model, making our approach applicable to different deep registration networks. Extensive experiments demonstrate that our diffusion registration framework presents outstanding pose estimation performance on the real-world TUD-L, LINEMOD, and Occluded-LINEMOD datasets. Code is available at https://***/JiangHB/DiffusionReg. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection

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arXiv 2024年

作者： Zhou, Fei Fu, Maixia Qian, Yulei Yang, Jian Dai, Yimian Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China Nanjing Marine Radar Institute Nanjing China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Infrared small target detection is crucial for the efficacy of infrared search and tracking systems. Current tensor decomposition methods emphasize representing small targets with sparsity but struggle to separate targets from complex backgrounds due to insufficient use of intrinsic directional information and reduced target visibility during decomposition. To address these challenges, this study introduces a Sparse Differential Directionality prior (SDD) framework. SDD leverages the distinct directional characteristics of targets to differentiate them from the background, applying mixed sparse constraints on the differential directional images and continuity difference matrix of the temporal component, both derived from Tucker decomposition. We further enhance target detectability with a saliency coherence strategy that intensifies target contrast against the background during hierarchical decomposition. A Proximal Alternating Minimization-based (PAM) algorithm efficiently solves our proposed model. Experimental results on several real-world datasets validate our method’s effectiveness, outperforming ten state-of-the-art methods in target detection and clutter suppression. Our code is available at https://***/GrokCV/SDD. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

Pick of the Bunch: Detecting Infrared Small Targets Beyond Hit-Miss Trade-Offs via Selective Rank-Aware Attention

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arXiv 2024年

作者： Dai, Yimian Pan, Peiwen Qian, Yulei Li, Yuxuan Li, Xiang Yang, Jian Wang, Huan PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security China The department of intelligence science School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Nanjing Marine Radar Institute Nanjing China VCIP CS Nankai University China The NKIARI Futian Shenzhen China

Infrared small target detection faces the inherent challenge of precisely localizing dim targets amidst complex background clutter. Traditional approaches struggle to balance detection precision and false alarm rates. To break this dilemma, we propose SeRankDet, a deep network that achieves high accuracy beyond the conventional hit-miss trade-off, by following the "Pick of the Bunch" principle. At its core lies our Selective Rank-Aware Attention (SeRank) module, employing a non-linear Top-K selection process that preserves the most salient responses, preventing target signal dilution while maintaining constant complexity. Furthermore, we replace the static concatenation typical in U-Net structures with our Large Selective Feature Fusion (LSFF) module, a dynamic fusion strategy that empowers SeRankDet with adaptive feature integration, enhancing its ability to discriminate true targets from false alarms. The network’s discernment is further refined by our Dilated Difference Convolution (DDC) module, which merges differential convolution aimed at amplifying subtle target characteristics with dilated convolution to expand the receptive field, thereby substantially improving target-background separation. Despite its lightweight architecture, the proposed SeRankDet sets new benchmarks in state-of-the-art performance across multiple public datasets. The code is available at https://***/GrokCV/SeRankDet. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

Generative Subgraph Contrast for Self-Supervised Graph Representation Learning

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arXiv 2022年

作者： Han, Yuehui Hui, Le Jiang, Haobo Qian, Jianjun Xie, Jin Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology China

Contrastive learning has shown great promise in the field of graph representation learning. By manually constructing positive/negative samples, most graph contrastive learning methods rely on the vector inner product based similarity metric to distinguish the samples for graph representation. However, the handcrafted sample construction (e.g., the perturbation on the nodes or edges of the graph) may not effectively capture the intrinsic local structures of the graph. Also, the vector inner product based similarity metric cannot fully exploit the local structures of the graph to characterize the graph difference well. To this end, in this paper, we propose a novel adaptive subgraph generation based contrastive learning framework for efficient and robust self-supervised graph representation learning, and the optimal transport distance is utilized as the similarity metric between the subgraphs. It aims to generate contrastive samples by capturing the intrinsic structures of the graph and distinguish the samples based on the features and structures of subgraphs simultaneously. Specifically, for each center node, by adaptively learning relation weights to the nodes of the corresponding neighborhood, we first develop a network to generate the interpolated subgraph. We then construct the positive and negative pairs of subgraphs from the same and different nodes, respectively. Finally, we employ two types of optimal transport distances (i.e., Wasserstein distance and Gromov-Wasserstein distance) to construct the structured contrastive loss. Extensive node classification experiments on benchmark datasets verify the effectiveness of our graph contrastive learning method. Source code is available at https://***/yh-han/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation

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arXiv 2022年

作者： He, Mu Hui, Le Bian, Yikai Ren, Jian Xie, Jin Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology China

Existing self-supervised monocular depth estimation methods can get rid of expensive annotations and achieve promising results. However, these methods suffer from severe performance degradation when directly adopting a model trained on a fixed resolution to evaluate at other different resolutions. In this paper, we propose a resolution adaptive self-supervised monocular depth estimation method (RA-Depth) by learning the scale invariance of the scene depth. Specifically, we propose a simple yet efficient data augmentation method to generate images with arbitrary scales for the same scene. Then, we develop a dual high-resolution network that uses the multi-path encoder and decoder with dense interactions to aggregate multi-scale features for accurate depth inference. Finally, to explicitly learn the scale invariance of the scene depth, we formulate a cross-scale depth consistency loss on depth predictions with different scales. Extensive experiments on the KITTI, Make3D and NYU-V2 datasets demonstrate that RA-Depth not only achieves state-of-the-art performance, but also exhibits a good ability of resolution adaptation. Source code is available at https://***/hmhemu/RA-Depth. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning